Excellence in knowledge or complete tool efficiency as the guiding principle for service processes?
The future of B2B service management is at a critical crossroads: Will service centers become obsolete with the adoption of Artificial Intelligence (AI) as tool providers like Microsoft bring AI closer to data and processes? Like envisioned by Satya Nadella in his AI-first vision? Or will AI drive the further adoption of the Shift Left approach?
This could happen by shifting complex tasks to earlier, customer-facing process stages, equipping service employees with more expertise to solve even complex problems faster, more efficiently, and closer to the customer. In this case, service centers would evolve into highly specialized knowledge hubs, consolidating and disseminating expert knowledge—supported, but not replaced, by AI!
Efficiency or expertise? This question concerns companies and investors alike. While AI has the potential to automate standardized service processes, the demand for individual support and deep domain knowledge in complex B2B environments is growing. Can the Shift Left approach meet these requirements while achieving efficiency gains?
For entrepreneurs and investors, looking ahead is particularly crucial: Is it already apparent which path will prevail? Can we predict what these changes will mean for companies investing in these technologies?
Agents as new service heroes – only for standardized tasks?
The automation of technical B2B processes promises radical efficiency gains. Particularly in industries with standardized, well-defined processes, this vision is no longer just a vision; it is already a reality:
- Standardization and self-healing systems: Processes such as IT support and cloud services can be almost fully automated with AI. Self-learning systems not only detect errors but also fix them independently—before customers even realize a problem has occurred.
- Cost reduction and scalability: Automation saves personnel and infrastructure costs. At the same time, AI can handle queries in the specific context of real-time guidance without additional investment, making it particularly attractive for high query volumes.
- Customer expectations for speed: In a 24/7 world, fully automated solutions offer consistent service quality—without wait times and in multiple languages.
Even as agent-based processing of service procedures is integrated into more B2B processes via major platform providers (like Microsoft, ServiceNow, Salesforce, or SAP), many areas that cannot be fully automated will remain. Highly specialized industries such as mechanical engineering, healthcare, or niche technologies require individual expertise and human judgment. Moreover, many AI systems are a “black box”: their decisions are often not transparent and can lead to trust issues in critical situations.
Shift Left: from service center to knowledge hub with a future
The Shift Left approach takes a different path. In addition to the clear efficiency gains that can be achieved, it focuses on strengthening competence and building knowledge repositories and knowledge centers. Here, the emphasis is on advancing knowledge management within organizations, which involves much more than simply collecting and providing information. Through feedback loops, knowledge from numerous queries is not only made available as needed but also curated, enriched, and improved over time. Knowledge management thus becomes a continuous process with humans in the loop for validating and ensuring the quality of new knowledge elements.
This also requires significant AI technology: General domain knowledge from generative AI models is combined via RAG (Retrieval-Augmented Generation) with customer-specific know-how, knowledge databases, and documents to deliver more precise and context-relevant responses.
With the Shift Left approach, companies can transform their organizations into the AI world in several steps without rebuilding all IT systems. Existing knowledge sources across various IT systems within the company are first tapped, and then the necessary processes for continuous improvement are established with the latest AI technology in collaboration with humans. There are now service providers that support customers in these steps and offer the entire process as a managed service.
Managing knowledge: when AI and humans learn together
Of course, major platform providers boldly promise efficiency improvements in processes solely through investments in licenses, subscriptions, and projects—replacing human-based support processes. But where exactly will this work?
How will it work, especially in high-volume, standardized processes with low customization rates? After all, process automation, and therefore the support process, must be updated through targeted projects and developers whenever processes change.
If processes change regularly, or if there is a completely normal variation in the technical machines used, the Shift Left approach is certainly more promising.
Building knowledge databases with a manual curation concept that involves humans is also compelling in industries with a high frequency of technological innovation.
It clearly depends on the structure of one’s processes whether investing in a fully automated platform and tool world or pursuing transformation through the Shift Left approach in an organizational project that retains and enhances the existing tool ecosystem is the better option.
The right path: platform automation or knowledge building?
Major platform providers like Microsoft, ServiceNow, and Salesforce would have us believe that their tools can solve everything. And yes, if your processes are clinically clean and standardized, that might be true. But let’s be honest: How many B2B realities look like that? Any company can count itself lucky if it can identify and adapt a few processes for AI agents.
The truth is, enough processes will remain for the pragmatic Shift Left approach. It combines the best of both worlds: AI-driven efficiency without eliminating the human factor. Knowledge management becomes a central asset, evolving with every customer interaction.
Projects of this nature are usually not feasible with outdated service providers that primarily rely on low-cost human resources in offshore locations. A holistic project approach is needed that leverages existing knowledge sources within the company and builds a new knowledge management process on top of them. This process should use modern AI tools to extract new knowledge from daily service processes. In this way, the available knowledge is continuously expanded and flexibly adapted to current requirements.
The choice is not between efficiency and expertise—it is about balance. Those who fail to understand this balance face the risk, in the AI frenzy, to automate processes that actually only needed an update with knowledge repositories and continuous development using a modern knowledge-AI stack.
